Medical imaging is the technique and process of creating visual representations of the interior of a body for clinical analysis and medical intervention, as well as visual representation of the function of some organs or tissues. Medical imaging seeks to reveal internal structures hidden by the skin and bones, as well as to aid in the diagnosis and treatment of diseases.
The advent of data-driven medicine and modern computing power has enabled patient-specific diagnosis and treatment based on medical imaging data. However, the primary bottle-neck in this workflow remains the ability to efficiently segment medical imaging data for use in simulation, modeling, visualization, animation and statistical analysis. Segmentation and visualization of medical image data such as MRI is a complex task. Manual image segmentation for a single CT or MRI scan is a complex process, often requiring expensive, specialized software and many hours of work to segment a single image sequence. As an image processing problem, medical image segmentation also poses many significant challenges due to noisy data, low contrast images, and large variations between patients. However, ultimately most segmentation implementations are trying to solve a single problem, which is classifying pixels of a medical image into some sort of anatomical structure or other anatomical abnormalities such as an injury or disease.
Using simple segmentation tasks such as using the threshold value of an image works fairly well with CT images. This is because CT images represent density of material similar to an X-ray image. Using threshold values may work for segmenting high density materials such as bones, but lacks the resolution to tell the differences between soft tissues. MRI imaging shows differences of soft tissues very well, but requires a more complex data driven approach to solving the classification problem.